Fake News Detection using Naive Bayes, Suppor...

Fake News Detection using Naive Bayes, Support Vector Machine, Neural network and Long Short-Term Memory

Author Name : Aditya Kocherlakota, Kotekal Saicharan, Shashank Gupta, Anayna Nidhi Singh, Surya Teja Karri, Trishala Reddy, Kanakagiri Sujay Ashrith

ABSTRACT

In this paper, we explore the application of Natural Language Processing techniques to identify when a news source may be producing fake news. We use a corpus of labelled real and fake new articles to build a classifier that can make decisions about information based on the content from the corpus. We use a text classification approach, using four different classification models, and analyze the results. The classification models are Naïve bayes, SVM, Neural network and LSTM. The best performing model was the LSTM implementation.

The model focuses on identifying fake news sources, based on multiple articles originating from a source. Once a source is labelled as a producer of fake news, we can predict with high confidence that any future articles from that source will also be fake news. Focusing on sources widens our article misclassification tolerance, because we then have multiple data points coming from each source.